1. Field of the Invention
Methods and apparatuses consistent with the present invention relate to encoding and decoding multi-view images, and more particularly, to a method and apparatus for encoding and decoding multi-view images with a high efficiency using a global disparity vector.
2. Description of the Related Art
Multi-view image encoding is performed by receiving image signals from a plurality of cameras that provide multi-view images and encoding the image signals. The multi-view images are compression-encoded using temporal correlation and spatial correlation between cameras (inter-view).
Temporal prediction is performed using temporal correlation between pictures at the same view-point, which have been taken at different times, and spatial prediction is performed using spatial correlation between pictures having different view-points, which have been taken at the same time.
A method of using such spatial correlation includes a prediction encoding method using a global disparity vector. The prediction encoding method using the global disparity vector will be described in detail with reference to FIGS. 1A and 1B, below.
FIGS. 1A and 1B are views for illustrating a global disparity between two images that are taken at different view-points.
FIGS. 1A and 1B show two images 110 and 130 having different view-points, which have been taken at the same time by two cameras that are positioned at different locations. In the case of multi-view images, multi-view images having different view-points have a global disparity due to the location difference of cameras which correspond to the different view-points. Referring to FIGS. 1A and 1B, the left part 120 of the image 110 illustrated in FIG. 1A is not seen in FIG. 1B. Also, the right part 140 of the image 130 illustrated in FIG. 1B is not seen in FIG. 1A. As a result, if the image 110 illustrated in FIG. 1A is shifted to the right side, the image 110 will be similar to the image 130 illustrated in FIG. 1B.
A global disparity between the image 110 of FIG. 1A and the image 130 of FIG. 1B is generated by the difference in locations of cameras that have photographed the corresponding scene at different view-points. A method of prediction-encoding a current picture using a vector representing such a global disparity is called “global disparity compensation”.
In global disparity compensation, a current picture is prediction-encoded with reference to a reference picture, wherein the reference picture is moved in a one-dimensional or two-dimensional direction by a global disparity between the reference picture and the current picture. However, the global disparity compensation does not consider the individual difference of each object which is included in the reference picture and the current picture.
FIGS. 2A, 2B, and 2C are views for illustrating disparities of objects included in images that have been captured at different view-points. As illustrated in FIG. 2A, a case where two cameras photograph objects that are spaced apart from each other will be described as an example, below.
An image photographed by a first camera 210 is illustrated in FIG. 2B, and an image photographed by a second camera 220 is illustrated in FIG. 2C. There is little difference in the appearance of an object 230 which is located near the cameras 210 and 220 between the images having the different view-points, while there is a great difference in the appearance of an object 240 which is relatively far from the cameras 210 and 220 between the images having the different view-points.
Although a global disparity between images having different view-points is considered when motion compensation is performed, objects included in the images having the different view-points can have different disparities other than a global disparity. Accordingly, a multi-view image encoding method considering such a problem is needed.